Imported version 1.0

.gitattributes

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+# Set default behaviour, in case users don't have core.autocrlf set.
+* text=auto
+
+# Explicitly declare text files we want to always be normalized and converted
+# to native line endings on checkout.
+*.c text
+*.h text
+*.mo text
+*.mos text
+*.order text
+
+# Denote all files that are truly binary and should not be modified.
+*.png binary
+*.jpg binary

.gitignore

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+*.mat
+
+*~
+
+buildlog.txt
+
+dsfinal.txt
+
+dsin.txt
+
+dslog.txt
+
+dsmodelext1.c
+
+dsmodel.c
+
+dymosim.exe
+
+dymosim.exp
+
+dymosim.lib

NeuralNetwork 1.0/Utilities/extractData.m

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+%EXTRACTDATA Create parameters code for Dymola NeuralNetwork Library.
+%
+%   EXTRACTDATA(FILENAME,LAYERNAME,MATRIXWEIGHT,MATRIXBIAS,ACTFUN,RECURRENT)
+%   creates a file named as FILENAME, in which is created the code for a
+%   NeuronNetwork layer.
+%
+%   If the NewronNetworkLayer is used the LAYERNAME has to be equal to
+%   'NoLayerName', otherwise as to be equal to the internal layer which
+%   parameters are referred.
+%
+%   If the script is used for a recurrent layer the parameter RECURRENT has
+%   to be equal to the number of non-recurrent inputs to the layer. For
+%   example, if the layer has 1 non-recurrent input and 5 neurons then the
+%   all input to the layer will be 6, but RECURRENT has to be equal to 1.
+
+%   ACTFUN can be equal to:
+%       'lin' for PureLin NeuralNetwork Activation Function
+%       'tan' for TanSig NeuralNetwork Activation Function
+%       'log' for LogSig NeuralNetwork Activation Function
+%       'rad' for RadBas NeuralNetwork Activation Function
+%       'noFun' is the activation function has not to be specified
+%
+%   Made by Fabio Codecà (http://www.elet.polimi.it/people/codeca)
+
+
+
+function extractData(fileName,layerName,matrixWeight,matrixBias,actFun,recurrent)
+
+if (strcmp('NoLayerName',layerName))
+    layerName='';
+else
+    layerName=strcat(layerName,'_');
+end
+
+fid = fopen(fileName,'w');
+Y = [matrixWeight];
+Z = [matrixBias];
+[numRow, numCol] = size(Y)
+
+if nargin == 5
+    numInput = numCol;
+else
+    numInput = recurrent;
+end
+
+fprintf(fid,strcat(layerName,'numNeurons = ',num2str(numRow),', \n'));
+fprintf(fid,strcat(layerName,'numInputs = ',num2str(numInput),', \n'));
+
+fprintf(fid,strcat(layerName,'weightTable = ['));
+% j row
+% i column
+for j=1:numRow
+    if (numCol==1)
+        if (numRow == 1)
+            fprintf(fid,'%8.8f ], \n',Y(j,1));
+        elseif (j == 1)
+            fprintf(fid,'%8.8f ; \n',Y(j,1));
+        elseif (j < numRow)
+            fprintf(fid,'\t %8.8f ; \n',Y(j,1));
+        else
+            fprintf(fid,'\t %8.8f ],\n',Y(j,1));
+        end
+    else
+        for i=1:numCol
+            if (j > 1 && i == 1)
+                fprintf(fid,'\t %8.8f ,',Y(j,i));
+            elseif (i==numCol)
+                if (j < numRow)
+                    fprintf(fid,'%8.8f ; \n',Y(j,i));
+                else
+                    fprintf(fid,'%8.8f ],\n',Y(j,i));
+                end
+            else
+                fprintf(fid,'%8.8f ,',Y(j,i));
+            end
+        end
+    end
+end
+
+numCol=1;
+fprintf(fid,strcat(layerName,'biasTable = ['));
+for j=1:numRow
+    if (numCol==1)
+        if (numRow == 1)
+            fprintf(fid,'%8.8f ]',Z(j,1));
+        elseif (j == 1)
+            fprintf(fid,'%8.8f ; \n',Z(j,1));
+        elseif (j < numRow)
+            fprintf(fid,'\t %8.8f ; \n',Z(j,1));
+        else
+            fprintf(fid,'\t %8.8f ]',Z(j,1));
+        end
+    else
+        for i=1:numCol
+            if (j > 1 && i == 1)
+                fprintf(fid,'\t %8.8f ,',Z(j,i));
+            elseif (i==numCol)
+                if (j < numRow)
+                    fprintf(fid,'%8.8f ; \n',Z(j,i));
+                else
+                    fprintf(fid,'%8.8f ]',Z(j,i));
+                end
+            else
+                fprintf(fid,'%8.8f ,',Z(j,i));
+            end
+        end
+    end
+end
+
+
+printFun=1;
+
+if (strcmp(actFun,'lin'))
+    fun='PureLin';
+elseif (strcmp(actFun,'tan'))
+    fun='TanSig';
+elseif (strcmp(actFun,'log'))
+    fun='LogSig';
+elseif (strcmp(actFun,'rad'))
+    fun='RadBas';
+elseif (strcmp(actFun,'noFun'))
+    printFun = 0;
+else
+    error('Activation Function Name is incorrect');
+end
+
+if(printFun)
+    fprintf(fid,strcat(', \n',layerName,'NeuronActivationFunction=NeuralNetwork.Types.ActivationFunction.',fun));
+end
+
+fclose(fid);